基于临床特征的胆道恶性肿瘤术前鉴别模型构建及表观性能评价OA
Development and Apparent Performance Evaluation of a Preoperative Discrimination Model for Biliary Tract Malignancies Based on Clinical Characteristics
目的 探讨胆道恶性肿瘤(BTC)与良性胆道手术患者术前鉴别诊断模型的建立及表观性能.方法 采用回顾性分析法,选取 2024 年 1 月至 2026 年 1 月期间本院收治的 90 例 BTC 患者设为病例组,选取同期在本院行胆道手术治疗的 90 例无恶性肿瘤良性胆道疾病患者纳入为对照组.对比两组临床、生化、肿瘤标志物(CA199、CA125、CEA)资料,将单因素分析存在统计学差异的项目纳入多因素 Logistic 回归分析模型,筛选与 BTC 病例状态相关的判别指标,基于多因素回归结果构建术前鉴别诊断列线图模型,绘制受试者操作特征(ROC)曲线,评估该模型判别性能.结果 病例组年龄更大,合并肝硬化、高血压、乙肝、糖尿病、胆管结石比例更高,HDL-C≤0.83 mmol/L、CA199升高、CA125 升高、CEA 升高占比高于对照组(P<0.05);经校正混杂因素的多因素 Logistic 回归分析显示,年龄、肝硬化、高血压、乙肝、糖尿病、胆管结石、HDL-C≤0.83 mmol/L、CA199 升高、CA125 升高、CEA 升高均为区分 BTC 与良性胆道疾病的独立判别指标(P<0.05);基于上述指标通过 R 软件"rms"程序包绘制术前鉴别诊断列线图模型,ROC 曲线分析显示,该判别模型表观 AUC 为 0.915(95%CI:0.876~0.955),特异度为 0.833,灵敏度为 0.878,约登指数为 0.711;本组数据 Hosmer-Lemeshow 检验仅提示建模样本内部拟合度尚可(P>0.05),不代表在普通人群或外部人群中鉴别发病校准可靠;建模样本校准曲线显示模型判别概率与本组实际病例分布具有一致性.结论 年龄、肝硬化、高血压、乙肝、糖尿病、胆管结石、低 HDL-C、CA199、CA125、CEA 是区分胆道手术患者中 BTC 与良性胆道病变的独立判别指标;基于上述指标构建的列线图术前鉴别模型本组内判别效能良好,可辅助临床医师在胆道疾病拟手术人群中区分 BTC 可疑病例.
Objective To develop a preoperative model for differentiating biliary tract cancers(BTC)from benign biliary diseases in patients undergoing biliary surgery and to evaluate its apparent performance.Methods This retrospective study included 90 patients with BTC admitted to the hospital from January 2024 to January 2026 as the case group and 90 patients with benign biliary diseases without malignancy who underwent biliary surgery during the same period as the control group.Clinical characteristics,biochemical indicators,and tumor markers[carbohydrate antigen 19-9(CA19-9),carbohydrate antigen 125(CA125),and carcinoembryonic antigen(CEA)]were compared between the two groups.Variables showing statistically significant differences in univariate analysis were entered into a multivariable logistic regression model to identify independent discriminative factors associated with BTC case status.A nomogram for preoperative differential diagnosis was constructed based on the multivariable regression results,and receiver operating characteristic(ROC)curve analysis was performed to assess the model's discriminative performance.Results Patients in the case group were older and had higher proportions of cirrhosis,hypertension,hepatitis B,diabetes mellitus,bile duct stones,HDL-C≤0.83 mmol/L,elevated CA19-9,elevated CA125,and elevated CEA than those in the control group(P<0.05).After adjustment for confounding factors,multivariable logistic regression analysis showed that age,cirrhosis,hypertension,hepatitis B,diabetes mellitus,bile duct stones,HDL-C≤0.83 mmol/L,elevated CA19-9,elevated CA125,and elevated CEA were independent discriminative factors for distinguishing BTC from benign biliary diseases(P<0.05).A preoperative diagnostic nomogram was constructed using the"rms"package in R based on these variables.ROC curve analysis showed that the model had an apparent AUC of 0.915(95%CI:0.876~0.955),with a specificity of 0.833,sensitivity of 0.878,and Youden index of 0.711.In this dataset,the Hosmer-Lemeshow test suggested acceptable internal model fit(P>0.05),but this does not indicate reliable calibration in the general population or in external populations.The calibration curve in the development sample showed agreement between model-predicted probabilities and the observed case distribution within this cohort.Conclusion Age,cirrhosis,hypertension,hepatitis B,diabetes mellitus,bile duct stones,low HDL-C,CA19-9,CA125,and CEA are independent discriminative factors for distinguishing BTC from benign biliary lesions among patients undergoing biliary surgery.The nomogram-based preoperative discrimination model constructed from these variables showed good apparent discrimination within the present cohort and may assist clinicians in identifying patients with suspected BTC among surgical candidates with biliary disease.
乔钢键;郭明浩;张贞;王清踪
南阳医学高等专科学校第一附属医院 普通外科七病区,河南 南阳 473000南阳医学高等专科学校第一附属医院 普通外科七病区,河南 南阳 473000南阳医学高等专科学校第一附属医院 普通外科七病区,河南 南阳 473000南阳医学高等专科学校第一附属医院 普通外科七病区,河南 南阳 473000
胆道恶性肿瘤列线图预测模型高血压糖尿病肿瘤标志物
biliary tract cancernomogram prediction modelhypertensiondiabetes mellitustumor markers
《临床研究》 2026 (9)
1-5,5
南阳市科技攻关计划项目(23KJGG190).
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